Overview

This role sits at the intersection of business, analytics and applied AI.

Responsibilities

• Automations that replace recurring manual workflows, from trigger through approval to delivery.
• A knowledge and retrieval layer over supply chain, commercial and consumer data, with grounded and cited outputs.
• The shared services these run on - deployment, monitoring, data access and validation.
• Build end-to-end automations that replace manual workflows - from trigger to delivery, with approval steps where needed.
• Build LLM agents and RAG systems over structured and unstructured data, with grounded and cited outputs.
• Write production SQL and Python to extract, transform and reconcile data - every output validated before it reaches a stakeholder.
• Deploy and maintain services on GCP with logging and alerting in place.
• Translate business rules into documented code that non-engineers can verify and sign off.

Qualifications

• Python - Production-grade Python: scheduled services, background workers, data pipelines and API integrations. Config-driven, tested and reliable. Not notebooks.
• SQL and BigQuery - Strong SQL for transformations, modelling and reconciliation at scale. Declares grain before joining, validates outputs against source.
• LLM engineering - Has shipped LLM applications to real users with tool calling and structured outputs. Treats prompts as code -versioned and tested against labelled samples. Accuracy measured before and after every change, not assumed.
• RAG and knowledge systems - Has built RAG systems in production: ingestion pipelines, embeddings, vector databases and hybrid search. Evaluates retrieval quality on a held-out set.
• Cloud and deployment - Has owned and operated services on GCP end to end. Responsible for production uptime, not just deployment.
• Data validation and testing - Writes automated checks for every pipeline: row counts, source reconciliation, freshness and edge cases. A system is not done until it can detect its own failures.
• Agent frameworks (LangGraph, CrewAI) or MCP servers.
• Event-driven and webhook integration patterns at production scale.
• Semantic layer or metrics catalog (dbt, LookML, Unity Catalog).
• Tech product or FMCG / consumer industry background.
• System integration — connecting AI outputs into existing tools and business systems (ERP, CRM, BI dashboards, internal platforms).
• Can read a data scientist's notebook, understand the logic and convert it into a production service.